Overview

Dataset statistics

Number of variables42
Number of observations179
Missing cells1032
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory166.8 KiB
Average record size in memory954.0 B

Variable types

Categorical20
DateTime2
Numeric20

Dataset

DescriptionQuality-verified clinical data for JHB_EZIN_025
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Alerts

study_source has constant value "JHB_EZIN_025"Constant
latitude has constant value "-26.2041"Constant
city has constant value "Johannesburg"Constant
province has constant value "Gauteng"Constant
country has constant value "South Africa"Constant
study_site_location has constant value "Johannesburg (General)"Constant
HIV_status has constant value "Positive"Constant
total_protein_extreme_flag has constant value "0.0"Constant
HEAT_STRESS_RISK_CATEGORY has constant value "LOW"Constant
climate_heat_day_p90 has constant value "0.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
BMI (kg/m²) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 7 other fieldsHigh correlation
HEAT_VULNERABILITY_SCORE is highly overall correlated with BMI (kg/m²) and 20 other fieldsHigh correlation
Other measures of obesity is highly overall correlated with BMI (kg/m²) and 7 other fieldsHigh correlation
Respiratory rate (breaths/min) is highly overall correlated with HEAT_VULNERABILITY_SCORE and 6 other fieldsHigh correlation
body_temperature_celsius is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
climate_14d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
climate_30d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
climate_7d_max_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_7d_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
climate_daily_max_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with HEAT_VULNERABILITY_SCORE and 13 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_p90_threshold is highly overall correlated with BMI (kg/m²) and 21 other fieldsHigh correlation
climate_p95_threshold is highly overall correlated with BMI (kg/m²) and 21 other fieldsHigh correlation
climate_p99_threshold is highly overall correlated with BMI (kg/m²) and 21 other fieldsHigh correlation
climate_season is highly overall correlated with HEAT_VULNERABILITY_SCORE and 15 other fieldsHigh correlation
climate_standardized_anomaly is highly overall correlated with HEAT_VULNERABILITY_SCORE and 14 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with HEAT_VULNERABILITY_SCORE and 9 other fieldsHigh correlation
heart_rate_bpm is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
height_m is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
jhb_subregion is highly overall correlated with BMI (kg/m²) and 8 other fieldsHigh correlation
longitude is highly overall correlated with BMI (kg/m²) and 8 other fieldsHigh correlation
month is highly overall correlated with HEAT_VULNERABILITY_SCORE and 5 other fieldsHigh correlation
respiration rate is highly overall correlated with HEAT_VULNERABILITY_SCORE and 6 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²) and 7 other fieldsHigh correlation
year is highly overall correlated with HEAT_VULNERABILITY_SCORE and 12 other fieldsHigh correlation
longitude is highly imbalanced (84.6%)Imbalance
jhb_subregion is highly imbalanced (84.6%)Imbalance
respiration rate has 129 (72.1%) missing valuesMissing
Other measures of obesity has 129 (72.1%) missing valuesMissing
BMI (kg/m²) has 129 (72.1%) missing valuesMissing
Respiratory rate (breaths/min) has 129 (72.1%) missing valuesMissing
heart_rate_bpm has 129 (72.1%) missing valuesMissing
weight_kg has 129 (72.1%) missing valuesMissing
height_m has 129 (72.1%) missing valuesMissing
body_temperature_celsius has 129 (72.1%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:35:12.442044
Analysis finished2025-11-25 05:35:26.111748
Duration13.67 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
JHB_EZIN_025
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_EZIN_025
2nd rowJHB_EZIN_025
3rd rowJHB_EZIN_025
4th rowJHB_EZIN_025
5th rowJHB_EZIN_025

Common Values

ValueCountFrequency (%)
JHB_EZIN_025179
100.0%

Length

2025-11-25T07:35:26.134683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.168112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_025179
100.0%

Most occurring characters

ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1253
58.3%
Decimal Number537
25.0%
Connector Punctuation358
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Decimal Number
ValueCountFrequency (%)
0179
33.3%
2179
33.3%
5179
33.3%
Connector Punctuation
ValueCountFrequency (%)
_358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1253
58.3%
Common895
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Common
ValueCountFrequency (%)
_358
40.0%
0179
20.0%
2179
20.0%
5179
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%
Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:35:26.205510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:26.336164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2021.0
148 
2020.0
31 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020.0
2nd row2020.0
3rd row2020.0
4th row2020.0
5th row2020.0

Common Values

ValueCountFrequency (%)
2021.0148
82.7%
2020.031
 
17.3%

Length

2025-11-25T07:35:26.382354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.419872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2021.0148
82.7%
2020.031
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0389
43.5%
2358
40.0%
1148
 
16.5%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0389
36.2%
2358
33.3%
.179
16.7%
1148
 
13.8%

month
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4860335
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:26.452717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0434264
Coefficient of variation (CV)0.55475899
Kurtosis-0.70995305
Mean5.4860335
Median Absolute Deviation (MAD)1
Skewness0.077888098
Sum982
Variance9.2624443
MonotonicityNot monotonic
2025-11-25T07:35:26.488249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
650
27.9%
731
17.3%
129
16.2%
215
 
8.4%
1012
 
6.7%
312
 
6.7%
510
 
5.6%
99
 
5.0%
115
 
2.8%
125
 
2.8%
ValueCountFrequency (%)
129
16.2%
215
 
8.4%
312
 
6.7%
41
 
0.6%
510
 
5.6%
650
27.9%
731
17.3%
99
 
5.0%
1012
 
6.7%
115
 
2.8%
ValueCountFrequency (%)
125
 
2.8%
115
 
2.8%
1012
 
6.7%
99
 
5.0%
731
17.3%
650
27.9%
510
 
5.6%
41
 
0.6%
312
 
6.7%
215
 
8.4%

latitude
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
-26.2041
179 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1432
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-26.2041
2nd row-26.2041
3rd row-26.2041
4th row-26.2041
5th row-26.2041

Common Values

ValueCountFrequency (%)
-26.2041179
100.0%

Length

2025-11-25T07:35:26.533802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.571635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
26.2041179
100.0%

Most occurring characters

ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1074
75.0%
Dash Punctuation179
 
12.5%
Other Punctuation179
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2358
33.3%
6179
16.7%
0179
16.7%
4179
16.7%
1179
16.7%
Dash Punctuation
ValueCountFrequency (%)
-179
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2358
25.0%
-179
12.5%
6179
12.5%
.179
12.5%
0179
12.5%
4179
12.5%
1179
12.5%

longitude
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
28.0473
175 
27.9394
 
4

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1253
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.0473
2nd row28.0473
3rd row28.0473
4th row28.0473
5th row28.0473

Common Values

ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Length

2025-11-25T07:35:26.608839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.644574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.0473175
97.8%
27.93944
 
2.2%

Most occurring characters

ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1074
85.7%
Other Punctuation179
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2179
16.7%
4179
16.7%
7179
16.7%
3179
16.7%
8175
16.3%
0175
16.3%
98
 
0.7%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2179
14.3%
.179
14.3%
4179
14.3%
7179
14.3%
3179
14.3%
8175
14.0%
0175
14.0%
98
 
0.6%

jhb_subregion
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Central_JHB
175 
Western_JHB
 
4

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1969
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral_JHB
2nd rowCentral_JHB
3rd rowCentral_JHB
4th rowCentral_JHB
5th rowCentral_JHB

Common Values

ValueCountFrequency (%)
Central_JHB175
97.8%
Western_JHB4
 
2.2%

Length

2025-11-25T07:35:26.681290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.715472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
central_jhb175
97.8%
western_jhb4
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e183
9.3%
n179
9.1%
t179
9.1%
r179
9.1%
_179
9.1%
J179
9.1%
H179
9.1%
B179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1074
54.5%
Uppercase Letter716
36.4%
Connector Punctuation179
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e183
17.0%
n179
16.7%
t179
16.7%
r179
16.7%
a175
16.3%
l175
16.3%
s4
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
J179
25.0%
H179
25.0%
B179
25.0%
C175
24.4%
W4
 
0.6%
Connector Punctuation
ValueCountFrequency (%)
_179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1790
90.9%
Common179
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e183
10.2%
n179
10.0%
t179
10.0%
r179
10.0%
J179
10.0%
H179
10.0%
B179
10.0%
C175
9.8%
a175
9.8%
l175
9.8%
Other values (2)8
 
0.4%
Common
ValueCountFrequency (%)
_179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e183
9.3%
n179
9.1%
t179
9.1%
r179
9.1%
_179
9.1%
J179
9.1%
H179
9.1%
B179
9.1%
C175
8.9%
a175
8.9%
Other values (3)183
9.3%

city
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
Johannesburg
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg
2nd rowJohannesburg
3rd rowJohannesburg
4th rowJohannesburg
5th rowJohannesburg

Common Values

ValueCountFrequency (%)
Johannesburg179
100.0%

Length

2025-11-25T07:35:26.753523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.785915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg179
100.0%

Most occurring characters

ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1969
91.7%
Uppercase Letter179
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n358
18.2%
o179
9.1%
h179
9.1%
a179
9.1%
e179
9.1%
s179
9.1%
b179
9.1%
u179
9.1%
r179
9.1%
g179
9.1%
Uppercase Letter
ValueCountFrequency (%)
J179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n358
16.7%
J179
8.3%
o179
8.3%
h179
8.3%
a179
8.3%
e179
8.3%
s179
8.3%
b179
8.3%
u179
8.3%
r179
8.3%

province
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Gauteng
179 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1253
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGauteng
2nd rowGauteng
3rd rowGauteng
4th rowGauteng
5th rowGauteng

Common Values

ValueCountFrequency (%)
Gauteng179
100.0%

Length

2025-11-25T07:35:26.823793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.858883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gauteng179
100.0%

Most occurring characters

ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1074
85.7%
Uppercase Letter179
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a179
16.7%
u179
16.7%
t179
16.7%
e179
16.7%
n179
16.7%
g179
16.7%
Uppercase Letter
ValueCountFrequency (%)
G179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1253
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G179
14.3%
a179
14.3%
u179
14.3%
t179
14.3%
e179
14.3%
n179
14.3%
g179
14.3%

country
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
South Africa
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth Africa
2nd rowSouth Africa
3rd rowSouth Africa
4th rowSouth Africa
5th rowSouth Africa

Common Values

ValueCountFrequency (%)
South Africa179
100.0%

Length

2025-11-25T07:35:26.893434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:26.928128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
south179
50.0%
africa179
50.0%

Most occurring characters

ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1611
75.0%
Uppercase Letter358
 
16.7%
Space Separator179
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o179
11.1%
u179
11.1%
t179
11.1%
h179
11.1%
f179
11.1%
r179
11.1%
i179
11.1%
c179
11.1%
a179
11.1%
Uppercase Letter
ValueCountFrequency (%)
S179
50.0%
A179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1969
91.7%
Common179
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S179
9.1%
o179
9.1%
u179
9.1%
t179
9.1%
h179
9.1%
A179
9.1%
f179
9.1%
r179
9.1%
i179
9.1%
c179
9.1%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S179
8.3%
o179
8.3%
u179
8.3%
t179
8.3%
h179
8.3%
179
8.3%
A179
8.3%
f179
8.3%
r179
8.3%
i179
8.3%
Other values (2)358
16.7%

date
Date

Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:35:26.964810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:27.011997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

study_site_location
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Johannesburg (General)
179 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3938
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJohannesburg (General)
2nd rowJohannesburg (General)
3rd rowJohannesburg (General)
4th rowJohannesburg (General)
5th rowJohannesburg (General)

Common Values

ValueCountFrequency (%)
Johannesburg (General)179
100.0%

Length

2025-11-25T07:35:27.056383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:27.089341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
johannesburg179
50.0%
general179
50.0%

Most occurring characters

ValueCountFrequency (%)
n537
13.6%
e537
13.6%
a358
 
9.1%
r358
 
9.1%
J179
 
4.5%
o179
 
4.5%
h179
 
4.5%
s179
 
4.5%
b179
 
4.5%
u179
 
4.5%
Other values (6)1074
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3043
77.3%
Uppercase Letter358
 
9.1%
Space Separator179
 
4.5%
Open Punctuation179
 
4.5%
Close Punctuation179
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n537
17.6%
e537
17.6%
a358
11.8%
r358
11.8%
o179
 
5.9%
h179
 
5.9%
s179
 
5.9%
b179
 
5.9%
u179
 
5.9%
g179
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
J179
50.0%
G179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%
Open Punctuation
ValueCountFrequency (%)
(179
100.0%
Close Punctuation
ValueCountFrequency (%)
)179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3401
86.4%
Common537
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n537
15.8%
e537
15.8%
a358
10.5%
r358
10.5%
J179
 
5.3%
o179
 
5.3%
h179
 
5.3%
s179
 
5.3%
b179
 
5.3%
u179
 
5.3%
Other values (3)537
15.8%
Common
ValueCountFrequency (%)
179
33.3%
(179
33.3%
)179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n537
13.6%
e537
13.6%
a358
 
9.1%
r358
 
9.1%
J179
 
4.5%
o179
 
4.5%
h179
 
4.5%
s179
 
4.5%
b179
 
4.5%
u179
 
4.5%
Other values (6)1074
27.3%

Clinical Study ID
Categorical

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size10.8 KiB
Arm B
39 
Arm A
36 
Arm D
36 
Arm C
35 
Arm E
33 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters895
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArm A
2nd rowArm B
3rd rowArm A
4th rowArm C
5th rowArm A

Common Values

ValueCountFrequency (%)
Arm B39
21.8%
Arm A36
20.1%
Arm D36
20.1%
Arm C35
19.6%
Arm E33
18.4%

Length

2025-11-25T07:35:27.124067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:27.163470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
arm179
50.0%
b39
 
10.9%
a36
 
10.1%
d36
 
10.1%
c35
 
9.8%
e33
 
9.2%

Most occurring characters

ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter358
40.0%
Lowercase Letter358
40.0%
Space Separator179
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A215
60.1%
B39
 
10.9%
D36
 
10.1%
C35
 
9.8%
E33
 
9.2%
Lowercase Letter
ValueCountFrequency (%)
r179
50.0%
m179
50.0%
Space Separator
ValueCountFrequency (%)
179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin716
80.0%
Common179
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A215
30.0%
r179
25.0%
m179
25.0%
B39
 
5.4%
D36
 
5.0%
C35
 
4.9%
E33
 
4.6%
Common
ValueCountFrequency (%)
179
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A215
24.0%
r179
20.0%
m179
20.0%
179
20.0%
B39
 
4.4%
D36
 
4.0%
C35
 
3.9%
E33
 
3.7%

Potassium (mEq/L)
Real number (ℝ)

Distinct30
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8128492
Minimum3.5
Maximum6.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.205201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.9
Q14.4
median4.8
Q35.1
95-th percentile5.9
Maximum6.6
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.60670619
Coefficient of variation (CV)0.12605967
Kurtosis0.32414825
Mean4.8128492
Median Absolute Deviation (MAD)0.4
Skewness0.53010157
Sum861.5
Variance0.3680924
MonotonicityNot monotonic
2025-11-25T07:35:27.247227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.716
 
8.9%
4.915
 
8.4%
4.815
 
8.4%
4.313
 
7.3%
4.411
 
6.1%
5.110
 
5.6%
4.610
 
5.6%
59
 
5.0%
5.29
 
5.0%
4.18
 
4.5%
Other values (20)63
35.2%
ValueCountFrequency (%)
3.51
 
0.6%
3.61
 
0.6%
3.72
 
1.1%
3.83
 
1.7%
3.96
3.4%
42
 
1.1%
4.18
4.5%
4.26
3.4%
4.313
7.3%
4.411
6.1%
ValueCountFrequency (%)
6.62
 
1.1%
6.41
 
0.6%
6.31
 
0.6%
6.23
1.7%
6.11
 
0.6%
5.93
1.7%
5.82
 
1.1%
5.75
2.8%
5.63
1.7%
5.56
3.4%

respiration rate
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.285632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-25T07:35:27.324033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

Other measures of obesity
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.367722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-25T07:35:27.414224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.577037251
 
0.6%
38.285672811
 
0.6%
18.185505681
 
0.6%
33.608396091
 
0.6%
21.254018291
 
0.6%
28.833153061
 
0.6%
25.683116171
 
0.6%
22.21074381
 
0.6%
32.510274321
 
0.6%
31.992171331
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

HIV_status
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
Positive
179 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1432
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowPositive
3rd rowPositive
4th rowPositive
5th rowPositive

Common Values

ValueCountFrequency (%)
Positive179
100.0%

Length

2025-11-25T07:35:27.463438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:27.500645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
positive179
100.0%

Most occurring characters

ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1253
87.5%
Uppercase Letter179
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i358
28.6%
o179
14.3%
s179
14.3%
t179
14.3%
v179
14.3%
e179
14.3%
Uppercase Letter
ValueCountFrequency (%)
P179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1432
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i358
25.0%
P179
12.5%
o179
12.5%
s179
12.5%
t179
12.5%
v179
12.5%
e179
12.5%

BMI (kg/m²)
Real number (ℝ)

High correlation  Missing 

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.538769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-25T07:35:27.584595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.577037251
 
0.6%
38.285672811
 
0.6%
18.185505681
 
0.6%
33.608396091
 
0.6%
21.254018291
 
0.6%
28.833153061
 
0.6%
25.683116171
 
0.6%
22.21074381
 
0.6%
32.510274321
 
0.6%
31.992171331
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

Respiratory rate (breaths/min)
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.622758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-25T07:35:27.658465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

heart_rate_bpm
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)68.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean78
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.698448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.45
Q170.25
median76
Q386
95-th percentile102.2
Maximum110
Range60
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.810673
Coefficient of variation (CV)0.17705991
Kurtosis-0.15961177
Mean78
Median Absolute Deviation (MAD)9
Skewness0.20889091
Sum3900
Variance190.73469
MonotonicityNot monotonic
2025-11-25T07:35:27.740755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
744
 
2.2%
673
 
1.7%
723
 
1.7%
832
 
1.1%
762
 
1.1%
862
 
1.1%
732
 
1.1%
822
 
1.1%
802
 
1.1%
642
 
1.1%
Other values (24)26
 
14.5%
(Missing)129
72.1%
ValueCountFrequency (%)
501
 
0.6%
511
 
0.6%
551
 
0.6%
561
 
0.6%
601
 
0.6%
631
 
0.6%
642
1.1%
661
 
0.6%
673
1.7%
701
 
0.6%
ValueCountFrequency (%)
1101
0.6%
1051
0.6%
1041
0.6%
1001
0.6%
991
0.6%
962
1.1%
941
0.6%
911
0.6%
891
0.6%
881
0.6%

weight_kg
Real number (ℝ)

High correlation  Missing 

Distinct47
Distinct (%)94.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean74.736
Minimum49.9
Maximum117.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.784770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum49.9
5-th percentile50.85
Q160.55
median73.1
Q384.9
95-th percentile107.7
Maximum117.8
Range67.9
Interquartile range (IQR)24.35

Descriptive statistics

Standard deviation17.108761
Coefficient of variation (CV)0.22892262
Kurtosis-0.14569641
Mean74.736
Median Absolute Deviation (MAD)12.05
Skewness0.57554181
Sum3736.8
Variance292.7097
MonotonicityNot monotonic
2025-11-25T07:35:27.831806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
902
 
1.1%
70.92
 
1.1%
73.12
 
1.1%
58.21
 
0.6%
83.91
 
0.6%
68.11
 
0.6%
85.31
 
0.6%
75.11
 
0.6%
68.81
 
0.6%
97.31
 
0.6%
Other values (37)37
 
20.7%
(Missing)129
72.1%
ValueCountFrequency (%)
49.91
0.6%
501
0.6%
50.41
0.6%
51.41
0.6%
53.81
0.6%
54.51
0.6%
54.61
0.6%
551
0.6%
56.21
0.6%
58.21
0.6%
ValueCountFrequency (%)
117.81
0.6%
112.41
0.6%
109.51
0.6%
105.51
0.6%
100.11
0.6%
97.31
0.6%
91.91
0.6%
902
1.1%
89.61
0.6%
88.41
0.6%

height_m
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)52.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.681
Minimum1.52
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:27.872535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile1.559
Q11.62
median1.68
Q31.75
95-th percentile1.79
Maximum1.87
Range0.35
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.080311892
Coefficient of variation (CV)0.047776259
Kurtosis-0.52690869
Mean1.681
Median Absolute Deviation (MAD)0.07
Skewness0.19250412
Sum84.05
Variance0.00645
MonotonicityNot monotonic
2025-11-25T07:35:27.912034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.754
 
2.2%
1.614
 
2.2%
1.724
 
2.2%
1.793
 
1.7%
1.643
 
1.7%
1.653
 
1.7%
1.683
 
1.7%
1.762
 
1.1%
1.632
 
1.1%
1.772
 
1.1%
Other values (16)20
 
11.2%
(Missing)129
72.1%
ValueCountFrequency (%)
1.521
 
0.6%
1.552
1.1%
1.571
 
0.6%
1.582
1.1%
1.591
 
0.6%
1.61
 
0.6%
1.614
2.2%
1.622
1.1%
1.632
1.1%
1.643
1.7%
ValueCountFrequency (%)
1.871
 
0.6%
1.851
 
0.6%
1.793
1.7%
1.781
 
0.6%
1.772
1.1%
1.762
1.1%
1.754
2.2%
1.731
 
0.6%
1.724
2.2%
1.711
 
0.6%

total_protein_extreme_flag
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:35:27.955133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:27.991397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

HEAT_VULNERABILITY_SCORE
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
0.0
129 
0.2624535785467304
50 

Length

Max length18
Median length3
Mean length7.1899441
Min length3

Characters and Unicode

Total characters1287
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.2624535785467304
2nd row0.2624535785467304
3rd row0.2624535785467304
4th row0.2624535785467304
5th row0.2624535785467304

Common Values

ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Length

2025-11-25T07:35:28.028832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:28.067242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0129
72.1%
0.262453578546730450
 
27.9%

Most occurring characters

ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1108
86.1%
Other Punctuation179
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
32.3%
4150
13.5%
5150
13.5%
2100
 
9.0%
6100
 
9.0%
3100
 
9.0%
7100
 
9.0%
850
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
27.8%
.179
13.9%
4150
11.7%
5150
11.7%
2100
 
7.8%
6100
 
7.8%
3100
 
7.8%
7100
 
7.8%
850
 
3.9%

HEAT_STRESS_RISK_CATEGORY
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
LOW
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOW
2nd rowLOW
3rd rowLOW
4th rowLOW
5th rowLOW

Common Values

ValueCountFrequency (%)
LOW179
100.0%

Length

2025-11-25T07:35:28.107048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:28.143001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
low179
100.0%

Most occurring characters

ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter537
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin537
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L179
33.3%
O179
33.3%
W179
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.188184
Minimum7.098
Maximum21.626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.172177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.098
5-th percentile7.098
Q17.098
median13.599
Q319.07
95-th percentile21.626
Maximum21.626
Range14.528
Interquartile range (IQR)11.972

Descriptive statistics

Standard deviation5.4715296
Coefficient of variation (CV)0.38563987
Kurtosis-1.5230969
Mean14.188184
Median Absolute Deviation (MAD)5.471
Skewness-0.083238863
Sum2539.685
Variance29.937636
MonotonicityNot monotonic
2025-11-25T07:35:28.207612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
21.62629
16.2%
19.0715
 
8.4%
18.28512
 
6.7%
16.65312
 
6.7%
13.59910
 
5.6%
17.6669
 
5.0%
18.3525
 
2.8%
19.3315
 
2.8%
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
13.59910
 
5.6%
16.1151
 
0.6%
16.65312
 
6.7%
17.6669
 
5.0%
18.28512
 
6.7%
18.3525
 
2.8%
19.0715
 
8.4%
19.3315
 
2.8%
ValueCountFrequency (%)
21.62629
16.2%
19.3315
 
2.8%
19.0715
8.4%
18.3525
 
2.8%
18.28512
 
6.7%
17.6669
 
5.0%
16.65312
 
6.7%
16.1151
 
0.6%
13.59910
 
5.6%
11.38131
17.3%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.255441
Minimum13.147
Maximum26.902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.242995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.147
5-th percentile13.147
Q113.147
median19.978
Q325.656
95-th percentile26.902
Maximum26.902
Range13.755
Interquartile range (IQR)12.509

Descriptive statistics

Standard deviation5.2852604
Coefficient of variation (CV)0.2609304
Kurtosis-1.4871955
Mean20.255441
Median Absolute Deviation (MAD)5.678
Skewness-0.18123378
Sum3625.724
Variance27.933978
MonotonicityNot monotonic
2025-11-25T07:35:28.277860image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
26.90229
16.2%
24.3615
 
8.4%
25.65612
 
6.7%
21.60712
 
6.7%
19.97810
 
5.6%
25.9319
 
5.0%
23.4215
 
2.8%
23.8895
 
2.8%
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
19.97810
 
5.6%
21.60712
 
6.7%
21.7511
 
0.6%
23.4215
 
2.8%
23.8895
 
2.8%
24.3615
 
8.4%
25.65612
 
6.7%
25.9319
 
5.0%
ValueCountFrequency (%)
26.90229
16.2%
25.9319
 
5.0%
25.65612
 
6.7%
24.3615
8.4%
23.8895
 
2.8%
23.4215
 
2.8%
21.7511
 
0.6%
21.60712
 
6.7%
19.97810
 
5.6%
18.231
17.3%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2291899
Minimum1.468
Maximum16.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.396351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.468
5-th percentile1.468
Q11.468
median7.473
Q314.512
95-th percentile16.62
Maximum16.62
Range15.152
Interquartile range (IQR)13.044

Descriptive statistics

Standard deviation5.6820579
Coefficient of variation (CV)0.69047597
Kurtosis-1.4289073
Mean8.2291899
Median Absolute Deviation (MAD)6.005
Skewness0.21403409
Sum1473.025
Variance32.285782
MonotonicityNot monotonic
2025-11-25T07:35:28.431515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
16.6229
16.2%
14.51215
 
8.4%
10.03812
 
6.7%
10.95212
 
6.7%
7.47310
 
5.6%
7.6469
 
5.0%
12.6975
 
2.8%
15.3835
 
2.8%
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
7.47310
 
5.6%
7.6469
 
5.0%
10.03812
 
6.7%
10.2571
 
0.6%
10.95212
 
6.7%
12.6975
 
2.8%
14.51215
 
8.4%
15.3835
 
2.8%
ValueCountFrequency (%)
16.6229
16.2%
15.3835
 
2.8%
14.51215
8.4%
12.6975
 
2.8%
10.95212
 
6.7%
10.2571
 
0.6%
10.03812
 
6.7%
7.6469
 
5.0%
7.47310
 
5.6%
4.96431
17.3%

climate_7d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.505022
Minimum8.244
Maximum20.221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.469544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.244
5-th percentile8.244
Q18.244
median13.294
Q318.832
95-th percentile19.788
Maximum20.221
Range11.977
Interquartile range (IQR)10.588

Descriptive statistics

Standard deviation4.5823236
Coefficient of variation (CV)0.3159129
Kurtosis-1.5240013
Mean14.505022
Median Absolute Deviation (MAD)5.05
Skewness-0.26204287
Sum2596.399
Variance20.99769
MonotonicityNot monotonic
2025-11-25T07:35:28.505947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8.24450
27.9%
12.94631
17.3%
19.78829
16.2%
18.83215
 
8.4%
18.60212
 
6.7%
17.59912
 
6.7%
13.29410
 
5.6%
16.8239
 
5.0%
18.1875
 
2.8%
20.2215
 
2.8%
ValueCountFrequency (%)
8.24450
27.9%
12.94631
17.3%
13.29410
 
5.6%
15.7421
 
0.6%
16.8239
 
5.0%
17.59912
 
6.7%
18.1875
 
2.8%
18.60212
 
6.7%
18.83215
 
8.4%
19.78829
16.2%
ValueCountFrequency (%)
20.2215
 
2.8%
19.78829
16.2%
18.83215
8.4%
18.60212
 
6.7%
18.1875
 
2.8%
17.59912
 
6.7%
16.8239
 
5.0%
15.7421
 
0.6%
13.29410
 
5.6%
12.94631
17.3%

climate_7d_max_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.690832
Minimum18.344
Maximum31.094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.541113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18.344
5-th percentile18.344
Q118.344
median22.614
Q327.183
95-th percentile31.094
Maximum31.094
Range12.75
Interquartile range (IQR)8.839

Descriptive statistics

Standard deviation4.1141998
Coefficient of variation (CV)0.17366211
Kurtosis-1.2017703
Mean23.690832
Median Absolute Deviation (MAD)4.27
Skewness0.029480078
Sum4240.659
Variance16.92664
MonotonicityNot monotonic
2025-11-25T07:35:28.578050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
18.34450
27.9%
22.61431
17.3%
27.18329
16.2%
24.97815
 
8.4%
31.09412
 
6.7%
26.4512
 
6.7%
21.40510
 
5.6%
26.729
 
5.0%
28.845
 
2.8%
29.2125
 
2.8%
ValueCountFrequency (%)
18.34450
27.9%
21.40510
 
5.6%
22.61431
17.3%
24.131
 
0.6%
24.97815
 
8.4%
26.4512
 
6.7%
26.729
 
5.0%
27.18329
16.2%
28.845
 
2.8%
29.2125
 
2.8%
ValueCountFrequency (%)
31.09412
 
6.7%
29.2125
 
2.8%
28.845
 
2.8%
27.18329
16.2%
26.729
 
5.0%
26.4512
 
6.7%
24.97815
8.4%
24.131
 
0.6%
22.61431
17.3%
21.40510
 
5.6%

climate_14d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.867106
Minimum9.443
Maximum20.751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.613221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.443
5-th percentile9.443
Q19.443
median14.097
Q318.834
95-th percentile20.751
Maximum20.751
Range11.308
Interquartile range (IQR)9.391

Descriptive statistics

Standard deviation4.4449059
Coefficient of variation (CV)0.29897586
Kurtosis-1.6672585
Mean14.867106
Median Absolute Deviation (MAD)4.654
Skewness0.0083155513
Sum2661.212
Variance19.757189
MonotonicityNot monotonic
2025-11-25T07:35:28.649119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9.44350
27.9%
11.88331
17.3%
20.75129
16.2%
18.55615
 
8.4%
18.83412
 
6.7%
18.02812
 
6.7%
14.09710
 
5.6%
16.0119
 
5.0%
19.9665
 
2.8%
19.4095
 
2.8%
ValueCountFrequency (%)
9.44350
27.9%
11.88331
17.3%
14.09710
 
5.6%
16.0119
 
5.0%
16.2821
 
0.6%
18.02812
 
6.7%
18.55615
 
8.4%
18.83412
 
6.7%
19.4095
 
2.8%
19.9665
 
2.8%
ValueCountFrequency (%)
20.75129
16.2%
19.9665
 
2.8%
19.4095
 
2.8%
18.83412
 
6.7%
18.55615
8.4%
18.02812
 
6.7%
16.2821
 
0.6%
16.0119
 
5.0%
14.09710
 
5.6%
11.88331
17.3%

climate_30d_mean_temp
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.231067
Minimum10.613
Maximum20.855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.684308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10.613
5-th percentile10.613
Q110.613
median14.519
Q319.102
95-th percentile20.855
Maximum20.855
Range10.242
Interquartile range (IQR)8.489

Descriptive statistics

Standard deviation4.3318452
Coefficient of variation (CV)0.28440852
Kurtosis-1.8128068
Mean15.231067
Median Absolute Deviation (MAD)3.906
Skewness0.071129169
Sum2726.361
Variance18.764883
MonotonicityNot monotonic
2025-11-25T07:35:28.718725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10.61350
27.9%
10.97831
17.3%
20.85529
16.2%
18.81515
 
8.4%
19.10212
 
6.7%
18.94212
 
6.7%
14.51910
 
5.6%
16.6769
 
5.0%
20.5285
 
2.8%
19.5445
 
2.8%
ValueCountFrequency (%)
10.61350
27.9%
10.97831
17.3%
14.51910
 
5.6%
16.2111
 
0.6%
16.6769
 
5.0%
18.81515
 
8.4%
18.94212
 
6.7%
19.10212
 
6.7%
19.5445
 
2.8%
20.5285
 
2.8%
ValueCountFrequency (%)
20.85529
16.2%
20.5285
 
2.8%
19.5445
 
2.8%
19.10212
 
6.7%
18.94212
 
6.7%
18.81515
8.4%
16.6769
 
5.0%
16.2111
 
0.6%
14.51910
 
5.6%
10.97831
17.3%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0242346
Minimum2.534
Maximum9.255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:28.750690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.534
5-th percentile2.534
Q12.534
median5.544
Q36.554
95-th percentile7.4253
Maximum9.255
Range6.721
Interquartile range (IQR)4.02

Descriptive statistics

Standard deviation2.0897289
Coefficient of variation (CV)0.4159298
Kurtosis-1.1912532
Mean5.0242346
Median Absolute Deviation (MAD)1.678
Skewness0.067239824
Sum899.338
Variance4.3669669
MonotonicityNot monotonic
2025-11-25T07:35:28.786744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2.53450
27.9%
7.22231
17.3%
6.04729
16.2%
5.54415
 
8.4%
6.55412
 
6.7%
2.66512
 
6.7%
5.45810
 
5.6%
9.2559
 
5.0%
2.8935
 
2.8%
4.3455
 
2.8%
ValueCountFrequency (%)
2.53450
27.9%
2.66512
 
6.7%
2.8935
 
2.8%
4.3455
 
2.8%
5.45810
 
5.6%
5.541
 
0.6%
5.54415
 
8.4%
6.04729
16.2%
6.55412
 
6.7%
7.22231
17.3%
ValueCountFrequency (%)
9.2559
 
5.0%
7.22231
17.3%
6.55412
 
6.7%
6.04729
16.2%
5.54415
8.4%
5.541
 
0.6%
5.45810
 
5.6%
4.3455
 
2.8%
2.8935
 
2.8%
2.66512
 
6.7%

climate_standardized_anomaly
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.20086034
Minimum-1.246
Maximum0.843
Zeros0
Zeros (%)0.0%
Negative99
Negative (%)55.3%
Memory size2.8 KiB
2025-11-25T07:35:28.821874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.246
5-th percentile-1.246
Q1-1.246
median-0.204
Q30.671
95-th percentile0.843
Maximum0.843
Range2.089
Interquartile range (IQR)1.917

Descriptive statistics

Standard deviation0.82121833
Coefficient of variation (CV)-4.0885042
Kurtosis-1.5735704
Mean-0.20086034
Median Absolute Deviation (MAD)0.875
Skewness-0.06388634
Sum-35.954
Variance0.67439955
MonotonicityNot monotonic
2025-11-25T07:35:28.854572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1.24650
27.9%
0.67131
17.3%
0.84329
16.2%
-0.43915
 
8.4%
-0.20412
 
6.7%
-0.89812
 
6.7%
0.3110
 
5.6%
0.0649
 
5.0%
-0.3365
 
2.8%
-0.2725
 
2.8%
ValueCountFrequency (%)
-1.24650
27.9%
-0.89812
 
6.7%
-0.43915
 
8.4%
-0.3365
 
2.8%
-0.2725
 
2.8%
-0.20412
 
6.7%
0.0649
 
5.0%
0.2711
 
0.6%
0.3110
 
5.6%
0.67131
17.3%
ValueCountFrequency (%)
0.84329
16.2%
0.67131
17.3%
0.3110
 
5.6%
0.2711
 
0.6%
0.0649
 
5.0%
-0.20412
 
6.7%
-0.2725
 
2.8%
-0.3365
 
2.8%
-0.43915
8.4%
-0.89812
 
6.7%

climate_heat_day_p90
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:35:28.895821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:28.931720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_day_p95
Categorical

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:35:28.970360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:29.005988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.58362
Minimum7.393
Maximum22.548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:29.034900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.393
5-th percentile7.393
Q17.393
median16.134
Q320.129
95-th percentile20.5932
Maximum22.548
Range15.155
Interquartile range (IQR)12.736

Descriptive statistics

Standard deviation5.5039646
Coefficient of variation (CV)0.37740729
Kurtosis-1.6187301
Mean14.58362
Median Absolute Deviation (MAD)4.811
Skewness-0.17802262
Sum2610.468
Variance30.293626
MonotonicityNot monotonic
2025-11-25T07:35:29.070108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
20.12929
16.2%
20.37615
 
8.4%
19.34312
 
6.7%
18.07812
 
6.7%
16.13410
 
5.6%
22.5489
 
5.0%
17.4245
 
2.8%
16.3575
 
2.8%
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
16.13410
 
5.6%
16.3575
 
2.8%
17.4245
 
2.8%
18.07812
 
6.7%
18.1951
 
0.6%
19.34312
 
6.7%
20.12929
16.2%
20.37615
 
8.4%
ValueCountFrequency (%)
22.5489
 
5.0%
20.37615
8.4%
20.12929
16.2%
19.34312
 
6.7%
18.1951
 
0.6%
18.07812
 
6.7%
17.4245
 
2.8%
16.3575
 
2.8%
16.13410
 
5.6%
11.32331
17.3%

climate_p90_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
28.409
129 
28.246
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.409
2nd row28.409
3rd row28.409
4th row28.409
5th row28.409

Common Values

ValueCountFrequency (%)
28.409129
72.1%
28.24650
 
27.9%

Length

2025-11-25T07:35:29.113382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:29.149863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
28.409129
72.1%
28.24650
 
27.9%

Most occurring characters

ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2229
25.6%
8179
20.0%
4179
20.0%
0129
14.4%
9129
14.4%
650
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2229
21.3%
8179
16.7%
.179
16.7%
4179
16.7%
0129
12.0%
9129
12.0%
650
 
4.7%

climate_p95_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
29.704
129 
29.513
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row29.704
2nd row29.704
3rd row29.704
4th row29.704
5th row29.704

Common Values

ValueCountFrequency (%)
29.704129
72.1%
29.51350
 
27.9%

Length

2025-11-25T07:35:29.189822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:29.228616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
29.704129
72.1%
29.51350
 
27.9%

Most occurring characters

ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2179
20.0%
9179
20.0%
7129
14.4%
0129
14.4%
4129
14.4%
550
 
5.6%
150
 
5.6%
350
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2179
16.7%
9179
16.7%
.179
16.7%
7129
12.0%
0129
12.0%
4129
12.0%
550
 
4.7%
150
 
4.7%
350
 
4.7%

climate_p99_threshold
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
31.797
129 
31.748
50 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31.797
2nd row31.797
3rd row31.797
4th row31.797
5th row31.797

Common Values

ValueCountFrequency (%)
31.797129
72.1%
31.74850
 
27.9%

Length

2025-11-25T07:35:29.268498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:29.305925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
31.797129
72.1%
31.74850
 
27.9%

Most occurring characters

ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number895
83.3%
Other Punctuation179
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7308
34.4%
3179
20.0%
1179
20.0%
9129
14.4%
450
 
5.6%
850
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1074
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7308
28.7%
3179
16.7%
1179
16.7%
.179
16.7%
9129
12.0%
450
 
4.7%
850
 
4.7%

climate_season
Categorical

High correlation 

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Winter
81 
Summer
49 
Spring
26 
Autumn
23 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Winter81
45.3%
Summer49
27.4%
Spring26
 
14.5%
Autumn23
 
12.8%

Length

2025-11-25T07:35:29.345678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:35:29.386195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
winter81
45.3%
summer49
27.4%
spring26
 
14.5%
autumn23
 
12.8%

Most occurring characters

ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter895
83.3%
Uppercase Letter179
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r156
17.4%
n130
14.5%
e130
14.5%
m121
13.5%
i107
12.0%
t104
11.6%
u95
10.6%
p26
 
2.9%
g26
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
W81
45.3%
S75
41.9%
A23
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

body_temperature_celsius
Real number (ℝ)

High correlation  Missing 

Distinct17
Distinct (%)34.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean36.488
Minimum35.2
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:35:29.424217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile35.9
Q136.2
median36.45
Q336.7
95-th percentile37.265
Maximum37.7
Range2.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46582821
Coefficient of variation (CV)0.012766614
Kurtosis0.904937
Mean36.488
Median Absolute Deviation (MAD)0.25
Skewness0.028784237
Sum1824.4
Variance0.21699592
MonotonicityNot monotonic
2025-11-25T07:35:29.461946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36.47
 
3.9%
36.77
 
3.9%
36.55
 
2.8%
36.34
 
2.2%
364
 
2.2%
36.13
 
1.7%
36.83
 
1.7%
36.23
 
1.7%
373
 
1.7%
37.42
 
1.1%
Other values (7)9
 
5.0%
(Missing)129
72.1%
ValueCountFrequency (%)
35.21
 
0.6%
35.51
 
0.6%
35.92
 
1.1%
364
2.2%
36.13
1.7%
36.23
1.7%
36.34
2.2%
36.47
3.9%
36.55
2.8%
36.61
 
0.6%
ValueCountFrequency (%)
37.71
 
0.6%
37.42
 
1.1%
37.12
 
1.1%
373
1.7%
36.91
 
0.6%
36.83
1.7%
36.77
3.9%
36.61
 
0.6%
36.55
2.8%
36.47
3.9%

Interactions

2025-11-25T07:35:25.141899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:12.973416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.642663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.352317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.977813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.578050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.248851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.889057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.492975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.197755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.789972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.427403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.054806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.751381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.373198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.981729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.668916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.255203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.865178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.534333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.169667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.013597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.672769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.382902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.006266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.606062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.279481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.916311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.520931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.225472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.819986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.456359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.084954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.781522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.401424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.011956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.697530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.282972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.892781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.563425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.198421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.057881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.702791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.412824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.036089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.634224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.309622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.947115image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.554632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.254182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.851145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.489284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.115905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.814664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.434058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.043594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.726796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.314671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.922389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.594981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.229540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.087803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.736601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.448786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.067629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.666680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.343164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.979887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.588343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.286152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.885608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.524707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.235249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.848916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.465108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.074996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.757361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.346110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.954618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.626198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.258176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.124915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.764359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.479505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.096541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.694837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.373315image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.012165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.618590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.316372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.915768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.554767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.263041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.880244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.493317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.102425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.785934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.375682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.983219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.656327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.286200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.154739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.791761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.512680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.125127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.723250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.404926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.040460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.647270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.344678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.946199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.584966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.290966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.910043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.522181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.130774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.813886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.404989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.011768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.683367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.319885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.212921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.824584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.547165image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.157800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.754986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.439737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.076035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.681710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.379073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.981446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.619286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.322150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.943901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.554157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.161970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.845652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.437044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.044151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.715853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.350240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.260448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.857455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.578252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.187522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.786844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.473276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.104199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.711636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.407750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.015474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.650639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.351006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.975727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.582770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.189818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.873587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.468202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.074583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.744659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.382323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.289606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.890678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.609999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.218223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.817268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.507122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.135381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.742355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.438733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.049203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.683045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.380158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.006459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.612309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.307276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.904876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.503167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.104591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.775793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.412198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.320233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.921642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.642429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.248664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.846217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.540693image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.164444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.774356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.466791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.081102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.716233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.410579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.037138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.644644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.336445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.933598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.536485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.134415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.804926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.444150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.350519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.956408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.676422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.280526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.879120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.574483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.197727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.807849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.500705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.115439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.747454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.442367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.067716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.676400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.367665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.963888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.567817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.165123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.836277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.476576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.381143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.988669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.710116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.313781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.909411image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.609404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.231057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.842035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.532099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.147443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.779660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.475347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.099214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.707687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.397306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.993978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.598309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.193381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.868298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.504712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.411292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.023011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.738292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.341113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.937332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.639467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.257742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.870745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.560613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.181742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.812408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.507603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.130319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.739128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.428971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.023916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.628317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.224232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.899705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.536991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.441045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.053940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.770687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.372990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.966733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.673678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.289832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.904912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.590413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.212687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.843328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.539574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.161995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.769106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.459228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.054937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.659512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.254065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.932104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.565109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.472737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.087410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.800273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.401359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.993943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.703368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.316782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.933419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.615694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.244521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.875180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.569927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.193291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.799845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.490658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.083748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.688964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.282869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.963125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.592431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.500688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.201657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.829095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.429651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.020974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.733563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.346349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.963617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.644018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.273889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.904064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.599823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.224119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.831808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.521750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.113853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.716949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.311225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.992883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.620216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.529908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.230149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.857037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.459401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.047713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.762952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.374284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.991331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.671457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.304829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.932617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.629654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.251917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.861749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.552181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.140656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.745825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.337213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.021490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.649317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.558825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.261250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.888366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.489447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.077964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.794842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.404169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.023503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.701690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.335363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.963486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.659410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.282619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.892817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.583891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.169148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.775276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.451748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.053406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.680803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.586298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.288919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.918371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.521265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.105750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.827279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.436096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.054088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.730833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.365361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.992630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.688879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.310988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.922549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.610043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.195117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.804256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.476087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.080970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.708991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:13.617169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.323830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:14.948346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:15.550334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.221834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:16.858253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:17.464368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.083542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:18.761682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:19.397048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.024796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:20.722021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.342862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:21.954680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:22.642217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.226624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:23.836881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:24.506523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:35:25.114239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:35:29.501708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BMI (kg/m²)Clinical Study IDHEAT_VULNERABILITY_SCOREOther measures of obesityPotassium (mEq/L)Respiratory rate (breaths/min)body_temperature_celsiusclimate_14d_mean_tempclimate_30d_mean_tempclimate_7d_max_tempclimate_7d_mean_tempclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonclimate_standardized_anomalyclimate_temp_anomalyheart_rate_bpmheight_mjhb_subregionlongitudemonthrespiration rateweight_kgyear
BMI (kg/m²)1.0000.0001.0001.0000.1170.005-0.1260.1510.151-0.2230.1020.2560.1590.1590.2021.0001.0001.0000.0000.256-0.0050.187-0.2981.0001.000-0.2480.0050.9100.000
Clinical Study ID0.0001.0000.0000.0000.1090.0000.0940.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0620.000
HEAT_VULNERABILITY_SCORE1.0000.0001.0001.0000.1281.0001.0000.7390.6850.7890.6390.8650.8270.8590.6350.3670.3670.3670.8040.7810.7981.0001.0000.0000.0000.8121.0001.0000.717
Other measures of obesity1.0000.0001.0001.0000.1170.005-0.1260.1510.151-0.2230.1020.2560.1590.1590.2021.0001.0001.0000.0000.256-0.0050.187-0.2981.0001.000-0.2480.0050.9100.000
Potassium (mEq/L)0.1170.1090.1280.1171.000-0.020-0.268-0.211-0.216-0.251-0.190-0.239-0.204-0.192-0.2430.3260.3260.3260.132-0.187-0.247-0.2750.1600.0000.000-0.055-0.0200.1820.058
Respiratory rate (breaths/min)0.0050.0001.0000.005-0.0201.0000.3270.3060.3060.0690.3120.0720.3180.318-0.1511.0001.0001.0000.1690.072-0.300-0.020-0.0141.0001.000-0.0591.0000.0080.114
body_temperature_celsius-0.1260.0941.000-0.126-0.2680.3271.0000.3150.315-0.1440.3010.3100.3370.3370.1111.0001.0001.0000.2180.310-0.1400.294-0.0971.0001.000-0.2880.327-0.1420.253
climate_14d_mean_temp0.1510.0000.7390.151-0.2110.3060.3151.0000.9950.9330.9810.9600.9870.9810.8640.9830.9830.9830.8620.6900.473-0.0470.0340.0000.000-0.3160.3060.2040.697
climate_30d_mean_temp0.1510.0000.6850.151-0.2160.3060.3150.9951.0000.9380.9700.9510.9720.9730.8430.6700.6700.6700.7520.6850.462-0.0470.0340.0720.072-0.3090.3060.2040.609
climate_7d_max_temp-0.2230.0000.789-0.223-0.2510.069-0.1440.9330.9381.0000.9040.9060.8980.8740.8120.9830.9830.9830.7820.6170.5700.1650.2010.0000.0000.0060.069-0.1490.823
climate_7d_mean_temp0.1020.0000.6390.102-0.1900.3120.3010.9810.9700.9041.0000.9450.9910.9870.8800.9860.9860.9860.8250.6560.458-0.1680.1640.0000.000-0.3350.3120.1900.539
climate_daily_max_temp0.2560.0000.8650.256-0.2390.0720.3100.9600.9510.9060.9451.0000.9700.9450.9400.9860.9860.9860.8310.7180.584-0.125-0.1990.0000.000-0.3100.0720.2080.703
climate_daily_mean_temp0.1590.0000.8270.159-0.2040.3180.3370.9870.9720.8980.9910.9701.0000.9900.9070.9860.9860.9860.9910.6910.491-0.0880.0550.0000.000-0.3610.3180.2190.908
climate_daily_min_temp0.1590.0000.8590.159-0.1920.3180.3370.9810.9730.8740.9870.9450.9901.0000.8860.9800.9800.9800.9730.6690.436-0.0880.0550.0000.000-0.4250.3180.2190.870
climate_heat_stress_index0.2020.0000.6350.202-0.243-0.1510.1110.8640.8430.8120.8800.9400.9070.8861.0000.9860.9860.9860.8590.5790.578-0.098-0.2790.0000.000-0.296-0.1510.0980.772
climate_p90_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9861.0000.9860.9860.6740.9860.7901.0001.0000.0890.0890.9801.0001.0000.259
climate_p95_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9860.9861.0000.9860.6740.9860.7901.0001.0000.0890.0890.9801.0001.0000.259
climate_p99_threshold1.0000.0000.3671.0000.3261.0001.0000.9830.6700.9830.9860.9860.9860.9800.9860.9860.9861.0000.6740.9860.7901.0001.0000.0890.0890.9801.0001.0000.259
climate_season0.0000.0000.8040.0000.1320.1690.2180.8620.7520.7820.8250.8310.9910.9730.8590.6740.6740.6741.0000.8600.6500.0000.0800.1040.1040.9600.1690.3020.901
climate_standardized_anomaly0.2560.0000.7810.256-0.1870.0720.3100.6900.6850.6170.6560.7180.6910.6690.5790.9860.9860.9860.8601.0000.807-0.125-0.1990.0000.000-0.1770.0720.2080.986
climate_temp_anomaly-0.0050.0000.798-0.005-0.247-0.300-0.1400.4730.4620.5700.4580.5840.4910.4360.5780.7900.7900.7900.6500.8071.0000.020-0.2040.0000.0000.249-0.300-0.1050.680
heart_rate_bpm0.1870.0001.0000.187-0.275-0.0200.294-0.047-0.0470.165-0.168-0.125-0.088-0.088-0.0981.0001.0001.0000.000-0.1250.0201.000-0.1401.0001.0000.084-0.0200.1230.000
height_m-0.2980.0001.000-0.2980.160-0.014-0.0970.0340.0340.2010.164-0.1990.0550.055-0.2791.0001.0001.0000.080-0.199-0.204-0.1401.0001.0001.0000.220-0.0140.0910.206
jhb_subregion1.0000.0000.0001.0000.0001.0001.0000.0000.0720.0000.0000.0000.0000.0000.0000.0890.0890.0890.1040.0000.0001.0001.0001.0000.8710.0001.0001.0000.000
longitude1.0000.0000.0001.0000.0001.0001.0000.0000.0720.0000.0000.0000.0000.0000.0000.0890.0890.0890.1040.0000.0001.0001.0000.8711.0000.0001.0001.0000.000
month-0.2480.0000.812-0.248-0.055-0.059-0.288-0.316-0.3090.006-0.335-0.310-0.361-0.425-0.2960.9800.9800.9800.960-0.1770.2490.0840.2200.0000.0001.000-0.059-0.1930.980
respiration rate0.0050.0001.0000.005-0.0201.0000.3270.3060.3060.0690.3120.0720.3180.318-0.1511.0001.0001.0000.1690.072-0.300-0.020-0.0141.0001.000-0.0591.0000.0080.114
weight_kg0.9100.0621.0000.9100.1820.008-0.1420.2040.204-0.1490.1900.2080.2190.2190.0981.0001.0001.0000.3020.208-0.1050.1230.0911.0001.000-0.1930.0081.0000.228
year0.0000.0000.7170.0000.0580.1140.2530.6970.6090.8230.5390.7030.9080.8700.7720.2590.2590.2590.9010.9860.6800.0000.2060.0000.0000.9800.1140.2281.000

Missing values

2025-11-25T07:35:25.773456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:35:25.966450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:35:26.069043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountrydatestudy_site_locationClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesityHIV_statusBMI (kg/m²)Respiratory rate (breaths/min)heart_rate_bpmweight_kgheight_mtotal_protein_extreme_flagHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonbody_temperature_celsius
1983JHB_EZIN_0252020-10-152020.010.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-15Johannesburg (General)Arm A3.520.018.577037Positive18.57703720.083.058.21.770.00.262454LOW18.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring36.5
1984JHB_EZIN_0252020-10-282020.010.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-28Johannesburg (General)Arm B4.622.020.822066Positive20.82206622.072.061.61.720.00.262454LOW18.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring36.1
1985JHB_EZIN_0252020-10-292020.010.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-10-29Johannesburg (General)Arm A4.919.022.308150Positive22.30815019.084.060.01.640.00.262454LOW18.28525.65610.03818.60231.09418.83419.1026.554-0.2040.00.019.34328.40929.70431.797Spring36.3
1986JHB_EZIN_0252020-11-042020.011.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-11-04Johannesburg (General)Arm C4.319.019.829482Positive19.82948219.074.051.41.610.00.262454LOW18.35223.42112.69718.18728.84019.96620.5282.893-0.3360.00.017.42428.40929.70431.797Spring36.7
1987JHB_EZIN_0252020-11-052020.011.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-11-05Johannesburg (General)Arm A4.720.025.307622Positive25.30762220.0105.068.91.650.00.262454LOW18.35223.42112.69718.18728.84019.96620.5282.893-0.3360.00.017.42428.40929.70431.797Spring37.1
1988JHB_EZIN_0252020-12-092020.012.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-09Johannesburg (General)Arm B4.918.032.841490Positive32.84149018.096.0112.41.850.00.262454LOW19.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer36.3
1989JHB_EZIN_0252020-12-112020.012.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-11Johannesburg (General)Arm E4.419.017.506390Positive17.50639019.064.050.01.690.00.262454LOW19.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer37.0
1990JHB_EZIN_0252020-12-152020.012.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2020-12-15Johannesburg (General)Arm D4.918.031.313449Positive31.31344918.050.0109.51.870.00.262454LOW19.33123.88915.38320.22129.21219.40919.5444.345-0.2720.00.016.35728.40929.70431.797Summer36.2
1991JHB_EZIN_0252021-01-052021.01.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-01-05Johannesburg (General)Arm B4.419.031.633715Positive31.63371519.080.076.01.550.00.262454LOW21.62626.90216.62019.78827.18320.75120.8556.0470.8430.00.020.12928.40929.70431.797Summer36.4
1992JHB_EZIN_0252021-01-052021.01.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-01-05Johannesburg (General)Arm C4.120.039.101562Positive39.10156220.070.0100.11.600.00.262454LOW21.62626.90216.62019.78827.18320.75120.8556.0470.8430.00.020.12928.40929.70431.797Summer36.3
study_sourceprimary_dateyearmonthlatitudelongitudejhb_subregioncityprovincecountrydatestudy_site_locationClinical Study IDPotassium (mEq/L)respiration rateOther measures of obesityHIV_statusBMI (kg/m²)Respiratory rate (breaths/min)heart_rate_bpmweight_kgheight_mtotal_protein_extreme_flagHEAT_VULNERABILITY_SCOREHEAT_STRESS_RISK_CATEGORYclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_7d_mean_tempclimate_7d_max_tempclimate_14d_mean_tempclimate_30d_mean_tempclimate_temp_anomalyclimate_standardized_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_p90_thresholdclimate_p95_thresholdclimate_p99_thresholdclimate_seasonbody_temperature_celsius
2152JHB_EZIN_0252021-06-152021.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-15Johannesburg (General)Arm B4.2NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaN
2153JHB_EZIN_0252021-06-162021.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-16Johannesburg (General)Arm B5.1NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaN
2154JHB_EZIN_0252021-07-012021.07.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-07-01Johannesburg (General)Arm E4.7NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW11.38118.2004.96412.94622.61411.88310.9787.2220.6710.00.011.32328.40929.70431.797WinterNaN
2155JHB_EZIN_0252021-07-012021.07.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-07-01Johannesburg (General)Arm D3.8NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW11.38118.2004.96412.94622.61411.88310.9787.2220.6710.00.011.32328.40929.70431.797WinterNaN
2156JHB_EZIN_0252021-05-132021.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-13Johannesburg (General)Arm A5.6NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaN
2157JHB_EZIN_0252021-05-202021.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-20Johannesburg (General)Arm B4.3NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaN
2158JHB_EZIN_0252021-05-272021.05.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-05-27Johannesburg (General)Arm D4.7NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW13.59919.9787.47313.29421.40514.09714.5195.4580.3100.00.016.13428.40929.70431.797AutumnNaN
2159JHB_EZIN_0252021-06-082021.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08Johannesburg (General)Arm E5.7NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaN
2160JHB_EZIN_0252021-06-082021.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08Johannesburg (General)Arm A5.4NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaN
2161JHB_EZIN_0252021-06-082021.06.0-26.204128.0473Central_JHBJohannesburgGautengSouth Africa2021-06-08Johannesburg (General)Arm C4.9NaNNaNPositiveNaNNaNNaNNaNNaN0.00.0LOW7.09813.1471.4688.24418.3449.44310.6132.534-1.2460.00.07.39328.24629.51331.748WinterNaN